Clustering-based methods are discriminative approaches aiming to estimate explicitly or implicitly decision boundaries for detecting anomalies [1]}, [2]}, [3]} as depicted in Eq. REF . One-Class Support Vector Machine (OC-SVM) [1]} is probably one of the most popular algorithms for anomaly detection. Its goal is to estimate the support of a high-dimensional distribution. This one-class classification method has been mainly used for detecting time-independent anomalies [5]}, [6]} but has also been employed for isolating outliers in time-series [7]}. Inspired by Support Vector Machines (SVM), Support Vector Data Description (SVDD) [3]} is another well-known method that is often used in the context of anomaly detection [9]}. Similar to SVM, kernels that map data representations to a higher dimensional space can be used. However, instead of relying on the estimation of a hyperplane, SVDD computes spherically shaped boundaries.
